Research Paper Volume 11, Issue 13 pp 4736—4756

Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis

Zhen-yu Song 1, , Fan Chao 1, , Zhiyuan Zhuo 1, , Zhe Ma 1, , Wenzhi Li 2, , Gang Chen 1, ,

  • 1 Department of Urology, Jinshan Hospital of Fudan University, Shanghai, China
  • 2 Department of Urology, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

received: March 8, 2019 ; accepted: July 4, 2019 ; published: July 15, 2019 ;

https://doi.org/10.18632/aging.102087
How to Cite

Copyright: Song et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The pathogenic mechanisms of prostate cancer (PCa) remain to be defined. In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate 10 eligible PCa microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between tumor samples and normal, matched specimens. To explore potential associations between gene sets and PCa clinical features and to identify hub genes, we utilized WGCNA to construct gene co-expression networks incorporating the DEGs screened with the use of RRA. From the key module, we selected LMNB1, TK1, ZWINT, and RACGAP1 for validation. We found that these genes were up-regulated in PCa samples, and higher expression levels were associated with higher Gleason scores and tumor grades. Moreover, ROC and K-M plots indicated these genes had good diagnostic and prognostic value for PCa. On the other hand, methylation analyses suggested that the abnormal up-regulation of these four genes likely resulted from hypomethylation, while GSEA and GSVA for single hub gene revealed they all had a close association with proliferation of PCa cells. These findings provide new insight into PCa pathogenesis, and identify LMNB1, TK1, RACGAP1 and ZWINT as candidate biomarkers for diagnosis and prognosis of PCa.

Abbreviations

GEO: Gene Expression Omnibus; PRAD: prostate adenocarcinoma; TCGA: the Cancer Genome Atlas; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; WGCNA: weighted gene co-expression network analysis; GS: gene significance; MM: module membership; TNM: Tumor Node Metastasis; GSEA: Gene Set Enrichment Analysis; GSVA: Gene Set Variation Analysis; DEG: differentially expressed gene; RRA: robust rank aggregation.